تمرین سری سوم: از لالیگا تا لیگ برتر
با استفاده از داده های لیگ دسته اول اسپانیا به سوالات زیر پاسخ دهید. از هر دو ابزار ggplot2 و highcharter برای این کار تصویرسازی استفاده نمایید.
۱. تعداد قهرمانی های تیم ها در تاریخ لالیگا را استخراج کرده و نمودار ستونی آنها را رسم کنید.
library(dplyr)
library(highcharter)
library(engsoccerdata)
library(ggplot2)
sf_db = as.tbl(spain)
#rbind two copies of the orignal df, simply reversing home/away team for each match
rbind(
sf_db %>%
filter(tier == 1) %>%
select(Season, team = home, opp = visitor, GF = hgoal, GA = vgoal),
sf_db %>%
filter(tier == 1) %>%
select(Season, team = visitor, opp = home, GF = vgoal, GA = hgoal)
) %>% mutate(GD = GF-GA) %>%
group_by(team,Season) %>%
summarize(GP = n(),
goalsF = sum(GF),
goalsA = sum(GA),
goaldif = sum(GD),
W = sum(GD>0),
D = sum(GD==0),
L = sum(GD<0)
) %>%
mutate(score = W*3 + D, t = 1) %>%
arrange(Season,desc(score), -goaldif) %>%
group_by(Season) %>%
mutate(rank = cumsum(t) %>% as.integer()) -> ll_table
ll_table %>%
filter(rank == 1) %>%
group_by(team) %>%
summarise(champ_count = n()) %>%
arrange(desc(champ_count))-> champs
hchart(champs, type = "column", hcaes(x = team, y = champ_count)) %>%
hc_title(text = "Championships Count Based on Teams") %>%
hc_xAxis(title = list(text = "Teams")) %>%
hc_yAxis(title = list(text = "Counts"))p = ggplot(champs , aes(x = team , y = champ_count)) + geom_bar(stat = "identity") + xlab("Teams") + ylab("Counts") + theme(axis.text.x = element_text(angle=80, vjust=0, hjust=0)) + ggtitle("Championships Count Based on Teams")
p
۲. کسل کننده ترین لیگ و تیم را بیابید. نمودار ده تیم و ده فصل کسل کننده را رسم کنید.
جواب : کسل کننده ترین تیم را تیمی در نظر میگیریم که کمترین گل را نسبت به تعداد بازی انجام داده اش به ثمر رسانده یا دریافت کرده است.
ll_table %>%
group_by(team) %>%
summarise(gb = sum(goalsA + goalsF), gpb = sum(GP)) %>%
mutate(boringness = gb/gpb) %>%
arrange(boringness) %>%
slice(1:10)-> boring_team
hchart(boring_team, type = "column", hcaes(x = team, y = boringness)) %>%
hc_title(text = "Boring Teams") %>%
hc_xAxis(title = list(text = "Teams")) %>%
hc_yAxis(title = list(text = "Average Goal in a Game"))p = ggplot(boring_team , aes(x = team , y = boringness)) + geom_bar(stat = "identity") + xlab("Teams") + ylab("Average Goal in a Game") + ggtitle("Boring Teams") + theme(axis.text.x = element_text(angle=80, vjust=0, hjust=0))
p
کسل کننده ترین فصل ها نیز به همین منوال کم گل ترین فصل ها نسبت به بازی های انجام شده در آن فصل درنظر گرفت.
ll_table %>%
group_by(Season) %>%
summarise(gis = sum(goalsA + goalsF), gpb = mean(GP), tb = n()) %>%
mutate(boringness = gis/(gpb*tb)) %>%
arrange(boringness) %>%
slice(1:10)-> boring_season
hchart(boring_season, type = "column", hcaes(x = as.character(Season), y = boringness)) %>%
hc_title(text = "Boring Seasons") %>%
hc_xAxis(title = list(text = "Seasons")) %>%
hc_yAxis(title = list(text = "Average Goal in a Game"))p = ggplot(boring_season , aes(x = as.character(Season) , y = boringness)) + geom_bar(stat = "identity") + xlab("Seasons") + ylab("Average Goal in a Game") + ggtitle("Boring Seasons") + theme(axis.text.x = element_text(angle=80, vjust=0, hjust=0))
p
***
۳. در چند درصد موارد قهرمان نیم فصل در پایان فصل قهرمان شده است؟
sf_edited = sf_db
sf_edited$ones <- 1
sf_edited %>%
group_by(Season) %>%
arrange(Date) %>%
mutate(id = cumsum(ones)) %>% # encounter games for finding half season
filter(id <= max(id)/2) -> sf_half
# create half season table
rbind(
sf_half %>%
filter(tier == 1) %>%
select(Season, team = home, opp = visitor, GF = hgoal, GA = vgoal),
sf_half %>%
filter(tier == 1) %>%
select(Season, team = visitor, opp = home, GF = vgoal, GA = hgoal)
) %>% mutate(GD = GF-GA) %>%
group_by(team,Season) %>%
summarize(GP = n(),
goalsF = sum(GF),
goalsA = sum(GA),
goaldif = sum(GD),
W = sum(GD>0),
D = sum(GD==0),
L = sum(GD<0)
) %>%
mutate(score = W*3 + D, t = 1) %>%
arrange(Season,desc(score), -goaldif) %>%
group_by(Season) %>%
mutate(rank = cumsum(t) %>% as.integer()) -> ll_table_half
merge(
ll_table %>%
filter(rank == 1) %>%
select(Season, f_c = team),
ll_table_half %>%
filter(rank == 1)%>%
select(Season, h_c = team)) -> half_and_full_champ
half_and_full_champ %>%
mutate(yes = ifelse((h_c == f_c), 1, 0)) %>%
group_by(yes) %>%
summarise(count = n()) -> stat_c_h_f
print(round((stat_c_h_f[[2]][2]/(stat_c_h_f[[2]][2]+stat_c_h_f[[2]][1]))*100)/100)[1] 0.51در حدود ۵۱ درصد از موارد قهرمان نیم فصل در پایان فصل قهرمان شده است.
۴. در بین سال های ۲۰۰۱ تا ۲۰۱۰ گربه سیاه تیم های بزرگ چه تیم هایی بوده است؟
چهار تیم مطرح در لالیگا را رئال مادرید، بارسلنا، اتلتیکومادرید و والنسیا فرض کرده ام زیرا بیشترین امتیازات را کسب کرده اند در بازه ی سال های ۲۰۰۱ تا ۲۰۱۱.
جواب: برای هرتیم بیشترین تعداد باخت از تیم های غیر از چهار تیم مطرح لالیگا ملاکی برای تعیین گربه سیاهی بوده است.
sf_db %>%
filter(2000< Season & Season <2011) %>%
mutate(hwin = ifelse((hgoal - vgoal)> 0 , 1 , 0)) %>%
filter(home == "FC Barcelona" & hwin == 0) %>%
select(teamw = visitor) %>%
filter(teamw != "Real Madrid" & teamw != "Atletico Madrid" & teamw !="Valencia CF") -> hlose
sf_db %>%
filter(2000< Season & Season <2011) %>%
mutate(vwin = ifelse((vgoal - hgoal)> 0 , 1 , 0)) %>%
filter(visitor == "FC Barcelona" & vwin == 0) %>%
select(teamw = home) %>%
filter(teamw != "Real Madrid" & teamw != "Atletico Madrid" & teamw !="Valencia CF") -> vlose
lose = rbind(vlose,hlose)
lose = as.data.frame(table(lose))
lose %>%
arrange(-Freq) %>%
slice(1:5) -> bc
ggplot(bc, aes(x = reorder(lose, Freq) , y = Freq, fill = Freq)) + geom_bar(stat = "identity") + xlab("Teams") + ylab("Count") + ggtitle("Black Cats of \'FC Barcelona\'")
hchart(bc, type = "column", hcaes(x = lose, y = Freq, fill = Freq)) %>%
hc_title(text = "Black Cats of \'FC Barcelona\'") %>%
hc_xAxis(title = list(text = "Teams")) %>%
hc_yAxis(title = list(text = "Count"))sf_db %>%
filter(2000< Season & Season <2011) %>%
mutate(hwin = ifelse((hgoal - vgoal)> 0 , 1 , 0)) %>%
filter(home == "Real Madrid" & hwin == 0) %>%
select(teamw = visitor) %>%
filter(teamw != "FC Barcelona" & teamw != "Atletico Madrid" & teamw !="Valencia CF") -> hlose
sf_db %>%
filter(2000< Season & Season <2011) %>%
mutate(vwin = ifelse((vgoal - hgoal)> 0 , 1 , 0)) %>%
filter(visitor == "Real Madrid" & vwin == 0) %>%
select(teamw = home) %>%
filter(teamw != "FC Barcelona" & teamw != "Atletico Madrid" & teamw !="Valencia CF") -> vlose
lose = rbind(vlose,hlose)
lose = as.data.frame(table(lose))
lose %>%
arrange(-Freq) %>%
slice(1:5) -> bc
ggplot(bc, aes(x = reorder(lose, Freq) , y = Freq, fill = Freq)) + geom_bar(stat = "identity") + xlab("Teams") + ylab("Count") + ggtitle("Black Cats of \'Real Madrid\'")
hchart(bc, type = "column", hcaes(x = lose, y = Freq, fill = Freq)) %>%
hc_title(text = "Black Cats of \'Real Madrid\'") %>%
hc_xAxis(title = list(text = "Teams")) %>%
hc_yAxis(title = list(text = "Count"))sf_db %>%
filter(2000< Season & Season <2011) %>%
mutate(hwin = ifelse((hgoal - vgoal)> 0 , 1 , 0)) %>%
filter(home == "Atletico Madrid" & hwin == 0) %>%
select(teamw = visitor) %>%
filter(teamw != "FC Barcelona" & teamw != "Real Madrid" & teamw !="Valencia CF") -> hlose
sf_db %>%
filter(2000< Season & Season <2011) %>%
mutate(vwin = ifelse((vgoal - hgoal)> 0 , 1 , 0)) %>%
filter(visitor == "Atletico Madrid" & vwin == 0) %>%
select(teamw = home) %>%
filter(teamw != "FC Barcelona" & teamw != "Real Madrid" & teamw !="Valencia CF") -> vlose
lose = rbind(vlose,hlose)
lose = as.data.frame(table(lose))
lose %>%
arrange(-Freq) %>%
slice(1:5) -> bc
ggplot(bc, aes(x = reorder(lose, Freq) , y = Freq, fill = Freq)) + geom_bar(stat = "identity") + xlab("Teams") + ylab("Count") + ggtitle("Black Cats of \'Atletico Madrid\'")
hchart(bc, type = "column", hcaes(x = lose, y = Freq, fill = Freq)) %>%
hc_title(text = "Black Cats of \'Atletico Madrid\'") %>%
hc_xAxis(title = list(text = "Teams")) %>%
hc_yAxis(title = list(text = "Count"))sf_db %>%
filter(2000< Season & Season <2011) %>%
mutate(hwin = ifelse((hgoal - vgoal)> 0 , 1 , 0)) %>%
filter(home == "Valencia CF" & hwin == 0) %>%
select(teamw = visitor) %>%
filter(teamw != "FC Barcelona" & teamw != "Atletico Madrid" & teamw !="Real Madrid") -> hlose
sf_db %>%
filter(2000< Season & Season <2011) %>%
mutate(vwin = ifelse((vgoal - hgoal)> 0 , 1 , 0)) %>%
filter(visitor == "Valencia CF" & vwin == 0) %>%
select(teamw = home) %>%
filter(teamw != "FC Barcelona" & teamw != "Atletico Madrid" & teamw !="Real Madrid") -> vlose
lose = rbind(vlose,hlose)
lose = as.data.frame(table(lose))
lose %>%
arrange(-Freq) %>%
slice(1:5) -> bc
ggplot(bc, aes(x = reorder(lose, Freq) , y = Freq, fill = Freq)) + geom_bar(stat = "identity") + xlab("Teams") + ylab("Count") + ggtitle("Black Cats of \'Valencia CF\'")
hchart(bc, type = "column", hcaes(x = lose, y = Freq, fill = Freq)) %>%
hc_title(text = "Black Cats of \'Valencia CF\'") %>%
hc_xAxis(title = list(text = "Teams")) %>%
hc_yAxis(title = list(text = "Count"))۵. در تاریخ لالیگا کدام تیم رکورددار زودترین قهرمانی است؟ همچنین کدام تیم مقتدرانه ترین قهرمانی را داشته است؟
rbind(
sf_db %>%
select(Season, team = home, opp = visitor, GF = hgoal, GA = vgoal, Date),
sf_db %>%
select(Season, team = visitor, opp = home, GF = vgoal, GA = hgoal, Date)
) %>% mutate(result = ifelse(((GF-GA)>0), "W", ifelse((GF == GA), "D", "L"))) %>%
group_by(Season, team) %>%
arrange(Season, team, Date) -> team_result
team_result %>%
mutate(score_m = ifelse(result == "L", 0 , ifelse(result == "W", 3 , 1)), gf = GF - GA) %>%
mutate(score_c = cumsum(score_m), goal_dif = cumsum(gf)) -> team_result
team_result %>%
ungroup() %>%
group_by(Season, team) %>%
arrange(Date) %>%
mutate(game_id = dense_rank(Date)) -> team_result
team_result %>%
ungroup() %>%
group_by(Season, game_id) %>%
arrange(Season,game_id, -score_c, -goal_dif) %>%
mutate(rank_c = row_number()) -> team_result
team_result %>%
ungroup() -> team_result
inner_join(
team_result %>%
group_by(Season, game_id) %>%
filter(rank_c == 1),
team_result %>%
group_by(Season, game_id) %>%
filter(rank_c == 2),
by = c("Season", "game_id")
) %>%
mutate(score_diff = score_c.x - score_c.y) %>%
select(Season, game_id, team = team.x, score_diff) %>%
ungroup() %>%
group_by(Season) %>%
mutate(remain_match = max(game_id) - game_id, is_champ = ifelse(remain_match*3 >= score_diff, "No", "Yes")) %>%
filter(is_champ == "Yes") %>%
ungroup() %>%
arrange(-remain_match,Season) %>%
filter(remain_match == max(remain_match))-> racing_ch
knitr::kable(racing_ch, "html")| Season | game_id | team | score_diff | remain_match | is_champ |
|---|---|---|---|---|---|
| 1960 | 25 | Real Madrid | 21 | 5 | Yes |
| 1962 | 25 | Real Madrid | 17 | 5 | Yes |
| 1973 | 29 | FC Barcelona | 16 | 5 | Yes |
| 1974 | 29 | Real Madrid | 16 | 5 | Yes |
| 1990 | 33 | FC Barcelona | 17 | 5 | Yes |
ll_table %>%
group_by(Season) %>%
arrange(-score) %>%
slice(1:2) %>%
mutate(s_diff = (max(score) - mean(score))*2) %>%
ungroup() %>%
arrange(-s_diff) %>%
filter(s_diff == max(s_diff)) %>%
slice(1:1) %>%
select(Team = team, Season, Score = score, Score_Diffrence = s_diff)-> most_champ
knitr::kable(most_champ, "html")| Team | Season | Score | Score_Diffrence |
|---|---|---|---|
| Real Madrid | 1962 | 72 | 21 |
۶. طولانی ترین نوار پیروزی مساوی و شکست مال چه تیم هایی است؟
rbind(
sf_db %>%
select(Season, team = home, opp = visitor, GF = hgoal, GA = vgoal, Date),
sf_db %>%
select(Season, team = visitor, opp = home, GF = vgoal, GA = hgoal, Date)
) %>% mutate(result = ifelse(((GF-GA)>0), "W", ifelse((GF == GA), "D", "L"))) %>%
group_by(Season, team) %>%
arrange(Season, team, Date) -> team_result_streak
team_result_streak$streak = sequence(rle(as.character(team_result_streak$result))$lengths)
team_result_streak %>%
group_by(result) %>%
select(team, Season, streak) %>%
arrange(-streak) %>%
slice(1:1) -> x
knitr::kable(x, "html")| result | team | Season | streak |
|---|---|---|---|
| D | Burgos CF | 1978 | 8 |
| L | UD Las Palmas | 1959 | 11 |
| W | FC Barcelona | 2010 | 16 |
طبق جدول بالا بیشترین نوار برد مطعلق به بارسلنا در سال ۲۰۱۰ است که ۱۶ برد متوالی کسب کرده است.
بیشترین نوار شکست نیز مطعلق به تیم Las Palmas در سال ۱۹۵۹ است که ۱۱ شکست متوالی را تجربه کرده است.
بیشترین نوار تساوی نیز مطعلق به تیم Burgos است که ۸ تساوی متوالی کسب کرده است.
۷. زودترین سقوط مال کدام تیم بوده است؟
inner_join(
team_result %>%
group_by(Season, game_id) %>%
filter(rank_c == max(rank_c)),
team_result %>%
group_by(Season, game_id) %>%
filter(rank_c == max(rank_c) - 3),
by = c("Season", "game_id")
) %>% mutate(score_diff = score_c.y - score_c.x) %>%
select(Season, game_id, team = team.x, score_diff) %>%
ungroup() %>%
group_by(Season) %>%
mutate(remain_match = max(game_id) - game_id, down_fall = ifelse(remain_match*3 >= score_diff, "No", "Yes")) %>%
filter(down_fall == "Yes") %>%
ungroup() %>%
arrange(-remain_match,Season) %>%
filter(remain_match == max(remain_match))-> racing_df_1
inner_join(
team_result %>%
group_by(Season, game_id) %>%
filter(rank_c == max(rank_c) - 1),
team_result %>%
group_by(Season, game_id) %>%
filter(rank_c == max(rank_c) - 3),
by = c("Season", "game_id")
) %>% mutate(score_diff = score_c.y - score_c.x) %>%
select(Season, game_id, team = team.x, score_diff) %>%
ungroup() %>%
group_by(Season) %>%
mutate(remain_match = max(game_id) - game_id, down_fall = ifelse(remain_match*3 >= score_diff, "No", "Yes")) %>%
filter(down_fall == "Yes") %>%
ungroup() %>%
arrange(-remain_match,Season) %>%
filter(remain_match == max(remain_match))-> racing_df_2
inner_join(
team_result %>%
group_by(Season, game_id) %>%
filter(rank_c == max(rank_c) - 2),
team_result %>%
group_by(Season, game_id) %>%
filter(rank_c == max(rank_c) - 3),
by = c("Season", "game_id")
) %>% mutate(score_diff = score_c.y - score_c.x) %>%
select(Season, game_id, team = team.x, score_diff) %>%
ungroup() %>%
group_by(Season) %>%
mutate(remain_match = max(game_id) - game_id, down_fall = ifelse(remain_match*3 >= score_diff, "No", "Yes")) %>%
filter(down_fall == "Yes") %>%
ungroup() %>%
arrange(-remain_match,Season) %>%
filter(remain_match == max(remain_match))-> racing_df_3
rbind(racing_df_1, racing_df_2, racing_df_3) %>%
arrange(-remain_match) %>%
slice(1:1) -> racing_df
knitr::kable(racing_df, "html")| Season | game_id | team | score_diff | remain_match | down_fall |
|---|---|---|---|---|---|
| 1997 | 31 | Sporting Gijon | 25 | 7 | Yes |
مانند شکل بالا تصویری از روند تغییر رتبه تیم ها در طول فصل ۱۹۹۸ رسم نمایید.
team_result %>%
filter(Season == 1998) %>%
select(team, rank_c, Date)-> team_result_1998
team_result_1998 %>%
hchart(type = "line", hcaes(x = datetime_to_timestamp(Date), y = rank_c, group = team)) %>%
hc_xAxis(type = "datetime", title = list(text = "date"), dateTimeLabelFormats = list(day = '%d of %b')) %>%
hc_yAxis(title = list(text = "Position"), reversed = TRUE, max = 20, tickInterval = 1, min = 1,
plotLines = list(list(color = "#FF0000", width = 2, value = 10, dashStyle = 'shortdash'))
) %>%
hc_title(text = "Team rankings in 1998/1999", style = list(fontWeight = "bold"))p = ggplot(data=team_result_1998, aes(x= Date, y=rank_c, colour=team)) +
geom_line() + scale_y_reverse(limit=c(20,0)) + xlab("Time") + ylab("Rank")
p
۹. جدولی مشابه بالا برای فصل ۲۰۱۲ از کل نتایج طراحی کنید.
sf_db %>%
filter(Season == 2012) %>%
arrange(home, visitor) %>%
mutate(result = ifelse(((hgoal - vgoal)>0), "W", ifelse((hgoal == vgoal), "D", "L"))) %>%
select(Season, Date, home, visitor, FT, result) -> sf_2012
library(forcats)
ggplot(sf_2012, aes(x = visitor,y = fct_rev(home))) +
geom_tile(colour="gray82", size=1.5, stat="identity", height=1, width=1) +
geom_text(data=sf_2012, aes(x = visitor, y = fct_rev(home), label = FT), color="white", size=rel(2)) +
scale_x_discrete(expand = c(0, 0), position = "top") +
scale_y_discrete(expand = c(0, 0)) +
xlab("") +
ylab("") +
ggtitle("Laliga Games 2012") +
theme(
strip.background = element_rect(fill="darkblue"),
strip.text = element_text(size=15, colour="white"),
strip.placement = "outside",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="gray50", size=0.5, linetype="solid"),
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text = element_text(color="black", size=rel(0.8)),
panel.background = element_rect(fill="gray99"),
plot.background = element_rect(fill="gray99"),
legend.position = "none",
axis.text.x = element_text(angle=80, vjust=0.5, hjust=0)
)
۱۰. سه آماره به همراه نمودار فردوسی پسند استخراج کنید.
۱. ماکسیمم تغییرات رتبه ی یک تیم در تمام فصل ها
این تغییرات تغییرات مثبت یا منفی است که بعد از بازی اول بدست آمده است.
team_result %>%
group_by(Season, team) %>%
filter(game_id != 1) %>%
mutate(dump_pump = max(rank_c) - min(rank_c)) %>%
summarise(diff_avg = mean(dump_pump)) %>%
ungroup() %>%
arrange(-diff_avg) %>%
slice(1:1) %>%
select(Season, Team = team, Difference = diff_avg) -> s_d
# use answer from above to calculate the result (use it below)
team_result %>%
filter(Season == 2008 & team == "Espanyol Barcelona") %>%
select(team, rank_c, Date)-> team_result_2008
team_result_2008 %>%
hchart(type = "line", hcaes(x = datetime_to_timestamp(Date), y = rank_c, group = team)) %>%
hc_xAxis(type = "datetime", title = list(text = "date"), dateTimeLabelFormats = list(day = '%d of %b')) %>%
hc_yAxis(title = list(text = "Position"), reversed = TRUE, max = 20, tickInterval = 1, min = 1,
plotLines = list(list(color = "#FF0000", width = 2, value = 10, dashStyle = 'shortdash'))
) %>%
hc_title(text = "Espanyol in 2008", style = list(fontWeight = "bold"))knitr::kable(s_d, "html")| Season | Team | Difference |
|---|---|---|
| 2008 | Espanyol Barcelona | 19 |
p = ggplot(data=team_result_2008, aes(x= Date, y=rank_c, colour=team)) +
geom_line() + scale_y_reverse(limit=c(20,0)) + xlab("Time") + ylab("Rank")
p
۲.میانگین امتیاز کسب شده ی هر تیم نسبت به بازی های انجام شده در هر لیگ.
ll_table %>%
ungroup() %>%
group_by(team) %>%
summarise(Average_Score = mean(score/GP)) %>%
arrange(-Average_Score) -> avg_score
ggplot(avg_score, aes(x = reorder(team, Average_Score), y = Average_Score, fill = Average_Score)) + geom_bar(stat = "identity") + xlab("Teams") + ylab("Average Score") + ggtitle("Average Score per Team per Game") + theme(axis.text.x = element_text(angle=80, vjust=0, hjust=0)) 
hchart(avg_score, type = "column", hcaes(x = team, y = Average_Score, fill = Average_Score)) %>%
hc_title(text = "Average Score per Team per Game") %>%
hc_xAxis(title = list(text = "Teams")) %>%
hc_yAxis(title = list(text = "Average Score"))۳. میانگین میزان گل زده در n امین بازی لیگ
team_result %>%
ungroup() %>%
group_by(Season, game_id, result) %>%
summarise(avg_goal = mean((GF+GA)/(2))) %>%
ungroup() %>%
mutate(avg_g = avg_goal/max(game_id))-> mean_league
p = ggplot(mean_league , aes(x = game_id , y = avg_g, fill = as.character(Season))) + geom_bar(stat = "identity") + xlab("Game ID") + ylab("Average Goal") + ggtitle("Average Goal per Game")
p
hchart(mean_league, type = "column", hcaes(x = game_id, y = avg_g, fill = as.character(Season))) %>%
hc_title(text = "Average Goal per Game") %>%
hc_xAxis(title = list(text = "Teams")) %>%
hc_yAxis(title = list(text = "Average Goal"))